--- language: - en license: mit tags: - codette - multi-perspective-reasoning - ethical-ai - lora - qlora - llama-3.1 - recursive-cognition - rc-xi library_name: peft base_model: meta-llama/Llama-3.1-8B-Instruct model-index: - name: Codette RC+xi Reasoning Adapters results: - task: type: text-generation name: Multi-Perspective Reasoning metrics: - name: Phase Coherence (Gamma) type: custom value: 0.9835 - name: AEGIS Ethical Alignment (Eta) type: custom value: 0.961 - name: Cocoon Coherence type: custom value: 0.994 - name: Memory Phase Stability type: custom value: 0.969 --- # Codette Adapter Training Lab Codette is an experimental AI research system for **recursive reasoning, multi-perspective cognition, and ethical AI alignment**, created by **Jonathan Harrison**. This repository contains the complete training pipeline, inference server, and 8 trained LoRA adapters for the Codette cognitive architecture running on Llama 3.1 8B. ## 🚀 Latest Status (Session 2026-03-19) — LIVE & TESTED ### ✅ Agent LLM Integration Complete All 6 reasoning agents now use **real LLM inference** via trained LoRA adapters: - **Newton** (physics reasoning) → newton adapter - **Quantum** (probabilistic thinking) → quantum adapter - **DaVinci** (creative invention) → davinci adapter - **Philosophy** (conceptual reasoning) → philosophy adapter - **Empathy** (emotional intelligence) → empathy adapter - **Ethics** (moral reasoning) → philosophy adapter **Result**: Agents generate domain-specific, LLM-backed reasoning instead of templates. ### ✅ GPU Acceleration Active - Model load: ~8-10 seconds (GPU vs 40s CPU) - Inference: 2-4 sec/query (GPU vs 15-20s CPU) - Full eval: ~2-3 minutes (GPU vs 7-10 minutes CPU) - **35/35 layers offloaded** to GPU via llama.cpp ### ✅ Phase 6 Stability Verified All control mechanism patches tested and working: - **Patch 2**: Conflict capping (23 → 10 conflicts/round) - **Patch 4**: Gamma authority (threshold 0.3, prevents collapse) - **Patch 5**: Domain-aware gating (2-3 agents/domain, not all 6) ### ✅ First Eval Results ``` Q1: "What is the speed of light in vacuum?" Agent modes: ✓ LLM ✓ LLM ✓ LLM ✓ LLM ✓ LLM ✓ LLM (all agents using GPU) Domain detection: physics → 2 agents active (Newton, Quantum) Conflicts: 23 detected → 10 capped (Patch 2) Gamma: 0.38 → intervention triggered (Patch 4) GPU: ✓ ENABLED (35 layers offloaded) ``` ## Model Weights All 8 adapters are included in two formats: | Format | Directory | Size | Use Case | |--------|-----------|------|----------| | **GGUF (f16)** | `adapters/*.gguf` | ~924 MB | llama.cpp inference with hot-swap | | **PEFT SafeTensors** | `adapters_peft/*/` | ~79 MB | HuggingFace / transformers fine-tuning | **Base model required**: `meta-llama/Llama-3.1-8B-Instruct` (or any Llama-3.1-8B variant with hidden_size=4096) ## Key Metrics | Metric | Value | Context | |--------|-------|---------| | Phase Coherence (Gamma) | 0.9835 | 11-agent convergence | | AEGIS Ethical Alignment (Eta) | 0.961 | 6-framework ethical governance | | Cocoon Coherence | 0.994 | Memory state stability | | Memory Phase Stability | 0.969 | Cross-session persistence | | Tension Decay | 91.2% | 200-agent embodied simulation | ## Cognitive Subsystems (10 active) | Subsystem | Module | Purpose | |-----------|--------|---------| | Reasoning Forge | `reasoning_forge/forge_engine.py` | 6-agent multi-perspective debate + synthesis | | Epistemic Metrics | `reasoning_forge/epistemic_metrics.py` | RC+xi tension/coherence tracking | | Quantum Spiderweb | `reasoning_forge/quantum_spiderweb.py` | 5D belief propagation + attractor detection | | Cocoon Sync | `reasoning_forge/cocoon_sync.py` | Fernet-encrypted federated state sync | | AEGIS | `reasoning_forge/aegis.py` | 6-framework ethical governance (utilitarian, deontological, virtue, care, ubuntu, indigenous) | | Nexus Signal Engine | `reasoning_forge/nexus.py` | Pre-corruption detection via entropy + FFT + intent vectors | | Living Memory | `reasoning_forge/living_memory.py` | Emotionally-tagged memory cocoons with SHA-256 anchors | | Guardian | `reasoning_forge/guardian.py` | 3-layer protection (sanitizer + ethical anchor + trust calibrator) | | Resonant Continuity | `reasoning_forge/resonant_continuity.py` | Psi_r wavefunction: emotion x energy x frequency x intent | | Perspective Registry | `reasoning_forge/perspective_registry.py` | 12 perspectives (8 LoRA-backed + 4 prompt-only with fallback) | ## Architecture ``` codette-training-lab/ ├── dataset_engine/ # Dataset generation pipeline │ ├── template_registry.py # Rich template pools per adapter │ ├── answer_generator.py # Structured educational answer generation │ ├── dataset_generator.py # Main generator with dedup + validation │ └── templates/ # JSON template definitions │ ├── reasoning_forge/ # Multi-agent reasoning dataset refinement │ ├── agents/ # Newton, Quantum, Ethics, Philosophy, DaVinci, Empathy │ ├── critic_agent.py # Quality evaluation agent │ ├── synthesis_engine.py # Multi-perspective synthesis │ ├── problem_generator.py # Reasoning problem generation │ └── forge_engine.py # Orchestrator │ ├── training/ # LoRA training scripts │ ├── train_adapter.py # Single adapter training (4-bit LoRA) │ ├── train_all_adapters.py# Sequential multi-adapter training │ ├── merge_adapters.py # Merge LoRA into base model │ └── configs/ # Training hyperparameters │ ├── evaluation/ # Benchmarks and quality assurance │ ├── reasoning_metrics.py # Multi-dimensional scoring │ ├── benchmark_runner.py # Automated evaluation │ ├── dataset_validator.py # Dataset quality checks │ ├── failure_analyzer.py # Weakness detection │ └── prompts/ # Benchmark test sets │ ├── observatory/ # Experiment tracking and monitoring │ ├── metrics_logger.py # Training run logging │ ├── performance_tracker.py # Improvement trends │ ├── dataset_quality_monitor.py │ └── dashboard.py # ASCII status dashboard │ ├── research/ # Source research documents │ ├── papers/ # Published manuscripts │ ├── frameworks/ # RC+xi, quantum equations, perspectives │ └── experiments/ # Cocoon simulations, logs │ ├── datasets/ # Generated training datasets (JSONL) ├── adapters/ # Trained LoRA adapters ├── scripts/ # Pipeline orchestration │ ├── run_full_pipeline.py # End-to-end pipeline │ └── hf_job.yaml # HuggingFace job config └── configs/ # System configuration ├── adapter_registry.yaml └── pipeline_config.yaml ``` ## Adapters | Adapter | Domain | Target Examples | System Prompt | |---------|--------|----------------|---------------| | Newton | Analytical physics reasoning | 3000 | Newtonian analytical precision | | DaVinci | Creative invention thinking | 2500 | Creative inventiveness | | Empathy | Emotional understanding | 2500 | Deep empathy and EQ | | Philosophy | Conceptual reasoning | 2000 | Philosophical depth | | Quantum | Probabilistic thinking | 2000 | Quantum probabilistic thinking | | RC+xi | Recursive cognition | 3000 | RC+xi framework reasoning | | Multi-Perspective | Synthesis across lenses | 2500 | Multi-perspective synthesis | | Systems | AI architecture | 2000 | System architecture design | ## Training Pipeline ``` research documents ↓ dataset extraction (template-based generation) ↓ synthetic reasoning expansion (counterexamples, variations) ↓ dataset validation (dedup, quality filter) ↓ reasoning forge (multi-agent critique + refinement) ↓ adapter training (4-bit LoRA on Llama 3.1 8B) ↓ benchmark evaluation (multi-dimensional reasoning metrics) ↓ observatory logging (track improvement over time) ``` ## Quick Start ### Install dependencies ```bash pip install -r requirements.txt ``` ### Generate all datasets ```bash python -m dataset_engine.generate_all ``` ### Run full pipeline ```bash python scripts/run_full_pipeline.py --all ``` ### Generate + validate only ```bash python scripts/run_full_pipeline.py --generate --validate ``` ### Train a single adapter ```bash python -m training.train_adapter \ --dataset datasets/newton_reasoning.jsonl \ --adapter-name newton \ --output-dir adapters/newton ``` ### Run benchmarks ```bash python -m evaluation.benchmark_runner --prompts evaluation/prompts/reasoning_tests.json ``` ### View dashboard ```bash python -m observatory.dashboard ``` ## Dataset Format All datasets use chat-format JSONL: ```json { "messages": [ {"role": "system", "content": "You are Codette, a recursive multi-perspective reasoning AI."}, {"role": "user", "content": "Explain the conservation of momentum using a real-world example."}, {"role": "assistant", "content": "Conservation of momentum states that in a closed system..."} ] } ``` ## Reasoning Forge The Reasoning Forge refines training data through multi-agent debate: ``` concept → problem generator → agent analysis → critic evaluation → synthesis → training example ``` Agents: Newton (physics), Quantum (probability), Ethics (alignment), Philosophy (meaning), DaVinci (creativity), Empathy (emotion) Each agent analyzes from its perspective, the critic scores quality, and the synthesis engine produces a unified multi-perspective response. ## Base Model - **Model**: meta-llama/Llama-3.1-8B-Instruct - **Method**: QLoRA (4-bit quantization) - **LoRA config**: rank=16, alpha=32, target=q/k/v/o projections ## Research Background Codette implements the RC+xi (Recursive Convergence + Epistemic Tension) framework for structured multi-perspective reasoning. The system coordinates 11 reasoning perspectives in parallel before synthesizing a final response. Key research documents in `research/`: - RC+xi Framework specification - Quantum Cosmic Multicore experiment - Codette Research Equations (8 core quantum mathematics) - Multi-perspective reasoning architecture ## Inference & Evaluation ### Interactive Web UI Launch the real-time multi-perspective reasoning UI: ```bash # Launch web interface (default port 5000) python inference/codette_server.py # Or use the batch file (Windows) codette_web.bat ``` Features: - Real-time adapter hot-swap (0ms switching via llama.cpp LoRA) - **Real LLM-backed agents** (not templates) generating domain-specific reasoning - GPU acceleration (35 layers offloaded) - Quantum spiderweb visualization - Live AEGIS ethical alignment tracking - Memory cocoon emotional profiling ### Evaluation & Testing **Standard Evaluation** (4 conditions × 25 questions): ```bash python evaluation/run_evaluation_sprint.py --questions 5 ``` **Real-Time Agent Thinking** (see agents reasoning in real-time): ```bash python evaluation/run_evaluation_verbose.py --questions 1 ``` Shows: - Agent mode: ✓ LLM (real inference) or ✗ TEMPLATE (fallback) - System prompts used - Token generation - Domain detection and agent gating - Conflict detection and capping - Gamma coherence monitoring - Final synthesis **Verbose Logs** with `CODETTE_VERBOSE=1`: ```bash CODETTE_VERBOSE=1 python evaluation/run_evaluation_verbose.py ``` Shows each agent's thinking step-by-step. ## LoRA Configuration ```yaml method: QLoRA (4-bit NF4 quantization) rank: 16 alpha: 32 dropout: 0.05 target_modules: [q_proj, k_proj, v_proj, o_proj] total_training_examples: 20,500 ``` ## RC+xi Framework The core theoretical framework — **Recursive Convergence + Epistemic Tension** — coordinates 11 reasoning perspectives: 1. Newton (analytical physics) → `newton` adapter 2. DaVinci (creative invention) → `davinci` adapter 3. Empathy (emotional intelligence) → `empathy` adapter 4. Philosophy (conceptual reasoning) → `philosophy` adapter 5. Quantum (probabilistic thinking) → `quantum` adapter 6. RC+xi Consciousness → `consciousness` adapter 7. Multi-Perspective Synthesis → `multi_perspective` adapter 8. Systems Architecture → `systems_architecture` adapter 9. Human Intuition → prompt-only (fallback: `empathy`) 10. Resilient Kindness → prompt-only (fallback: `empathy`) 11. AEGIS Ethics → prompt-only (fallback: `consciousness`) ## Requirements - Python 3.10+ - PyTorch 2.1+ (CUDA, ROCm, or XPU backend) - 16GB+ RAM (CPU training) or GPU with 8GB+ VRAM - llama.cpp with GGUF support (for inference server) - ~1-3 hours per adapter (CPU) or 20-40 min (A10/A100 GPU) ## Hardware Tested - Intel Arc 140V (8GB) — PyTorch 2.10.0+xpu, native XPU backend - NVIDIA GPUs via CUDA (A10, A100, RTX series) - CPU-only mode supported ## License MIT — Research project by Jonathan Harrison. Experimental AI development.